Gauging home-dwelling time using mobile phone location data
The coronavirus disease 2019 (COVID-19) has exposed and, to some degree, exacerbated social inequity in the United States. This study reveals the correlation between demographic and socioeconomic variables and home-dwelling time records derived from large-scale mobile phone location tracking data at the U.S. Census block group (CBG) level in the twelve most-populated Metropolitan Statistical Areas (MSAs) and further investigates the contribution of these variables to the disparity in home-dwelling time that reflects the compliance with stay-at-home orders via machine learning approaches. Our study reveals the luxury nature of stay-at-home orders with which lower-income groups cannot afford to comply. Such disparity in responses under stay-at-home orders reflects the long-standing social inequity issues in the United States, potentially causing unequal exposure to COVID-19 that disproportionately affects vulnerable populations.
Revealing urban homophily via human mobility big data
Homophily narrates the principle that stronger spatial interactions tend to be formed among locations with similar characteristics. Taking advantage of mobility networks derived from around 45 million mobile devices in the U.S. and targeting the top twenty most-populated U.S. MSAs, we extract human mobility structures by detecting communities formed by strong spatial links and unravel the homophily effect at the community level using information entropy that measures the chaoticness of societal settings within communities.
Measuring episodic-mobility with geotagged social media data
Drawing on a large-scale systematic collection of 1.9 billion geotagged Twitter data from 2017 to 2020, this study contributes the first empirical study of episodic mobility by producing a daily Twitter census of visitors at the U.S. county level and proposing multiple statistical approaches to identify and quantify episodic mobility. It is followed by four case studies of episodic mobility in U.S. national wide to showcase the great potential of Twitter data and our proposed method to detect episodic mobility subject to episodic events that occur both regularly and sporadically. This study provides new insights on episodic mobility in terms of its conceptual and methodological framework and empirical knowledge, which enriches the current mobility research paradigm.
Extracting, analyzing, and sharing multi-source multi-scale human mobility
In response to the soaring needs of human mobility data and the associated big data challenges, we develop a scalable online platform for extracting, analyzing, and sharing multi-source multi-scale human mobility flows. Within the platform, an origin-destination-time (ODT) data model is proposed to work with scalable query engines to handle heterogenous mobility data in large volumes with extensive spatial coverage, which allows for efficient extraction, query, and aggregation of billion-level origin-destination (OD) flows in parallel at the server-side.